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Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding by Li, Hongqi; Zhang, Haodong; Chen, Yitong is a scholarly article available to read on EtoBox.

What is Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding about?

The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-o

Author

Li, Hongqi; Zhang, Haodong; Chen, Yitong

Published

2024

Language

EN